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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.21553 |
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| _version_ | 1866915858179162112 |
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| author | Jaber, Ahmed Zhu, Wangshu Roy, Ayon Jayavelu, Karthick Downes, Justin Mohamed, Sameer Agonafir, Candace Hawkins, Linnia Zheng, Tian |
| author_facet | Jaber, Ahmed Zhu, Wangshu Roy, Ayon Jayavelu, Karthick Downes, Justin Mohamed, Sameer Agonafir, Candace Hawkins, Linnia Zheng, Tian |
| contents | Climate data science remains constrained by fragmented data sources, heterogeneous formats, and steep technical expertise requirements. These barriers slow discovery, limit participation, and undermine reproducibility. We present AutoClimDS, a Minimum Viable Product (MVP) Agentic AI system that addresses these challenges by integrating a curated climate knowledge graph (KG) with a set of Agentic AI workflows designed for cloud-native scientific analysis. The KG unifies datasets, metadata, tools, and workflows into a machine-interpretable structure, while AI agents, powered by generative models, enable natural-language query interpretation, automated data discovery, programmatic data acquisition, and end-to-end climate analysis. A key result is that AutoClimDS can reproduce published scientific figures and analyses from natural-language instructions alone, completing the entire workflow from dataset selection to preprocessing to modeling. When given the same tasks, state-of-the-art general-purpose LLMs (e.g., ChatGPT GPT-5.1) cannot independently identify authoritative datasets or construct valid retrieval workflows using standard web access. This highlights the necessity of structured scientific memory for agentic scientific reasoning. By encoding procedural workflow knowledge into a KG and integrating it with existing technologies (cloud APIs, LLMs, sandboxed execution), AutoClimDS demonstrates that the KG serves as the essential enabling component, the irreplaceable structural foundation, for autonomous climate data science. This approach provides a pathway toward democratizing climate research through human-AI collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21553 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoClimDS: Climate Data Science Agentic AI -- A Knowledge Graph is All You Need Jaber, Ahmed Zhu, Wangshu Roy, Ayon Jayavelu, Karthick Downes, Justin Mohamed, Sameer Agonafir, Candace Hawkins, Linnia Zheng, Tian Artificial Intelligence Computational Engineering, Finance, and Science Human-Computer Interaction Machine Learning Multiagent Systems Climate data science remains constrained by fragmented data sources, heterogeneous formats, and steep technical expertise requirements. These barriers slow discovery, limit participation, and undermine reproducibility. We present AutoClimDS, a Minimum Viable Product (MVP) Agentic AI system that addresses these challenges by integrating a curated climate knowledge graph (KG) with a set of Agentic AI workflows designed for cloud-native scientific analysis. The KG unifies datasets, metadata, tools, and workflows into a machine-interpretable structure, while AI agents, powered by generative models, enable natural-language query interpretation, automated data discovery, programmatic data acquisition, and end-to-end climate analysis. A key result is that AutoClimDS can reproduce published scientific figures and analyses from natural-language instructions alone, completing the entire workflow from dataset selection to preprocessing to modeling. When given the same tasks, state-of-the-art general-purpose LLMs (e.g., ChatGPT GPT-5.1) cannot independently identify authoritative datasets or construct valid retrieval workflows using standard web access. This highlights the necessity of structured scientific memory for agentic scientific reasoning. By encoding procedural workflow knowledge into a KG and integrating it with existing technologies (cloud APIs, LLMs, sandboxed execution), AutoClimDS demonstrates that the KG serves as the essential enabling component, the irreplaceable structural foundation, for autonomous climate data science. This approach provides a pathway toward democratizing climate research through human-AI collaboration. |
| title | AutoClimDS: Climate Data Science Agentic AI -- A Knowledge Graph is All You Need |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Human-Computer Interaction Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2509.21553 |